The field of medical product development is constantly evolving, with patient-centered approaches gaining importance. Patients’ experiences and perspectives are invaluable in understanding the efficacy and impact of medical interventions. Traditionally, clinical trials and research studies have relied on clinician-reported data and objective measurements. However, in recent years, inclusion of patient-reported outcomes (PROs) has emerged as a valuable resource to inform all stages of the product development life cycle. What is the significance of patient self-reported data in supporting medical product development and how it can positively impact key stages of the development process?

What are Patient Reported Outcomes (PRO)?

A patient-reported outcome (PRO) is data obtained directly from patients and is based on a patient’s perception of a disease and its treatment. PROs are important because they provide a patient perspective on a disease/treatment that might not be captured by a clinical measurement. This information is collected through validated questionnaires or surveys, where patients self-report their experiences; these responses come directly from the patient, without interpretation of the patient’s response by a clinician or anyone else. Traditionally, PROs have been collected using a paper-based questionnaire, but as with most paper forms, many labs have recently migrated to electronic forms, also known as electronic Patient Reported Outcomes (ePRO). PROs offer a unique perspective as they reflect the patients’ feelings, preferences, and day-to-day experiences with a medical condition or treatment. Some examples of PROs include pain levels, physical functioning, mental health, and overall satisfaction with treatment.

How are Patient Reported Outcomes used in the Product Development Life Cycle?

1. Conceptualization (Pre-clinical): 

The product development life cycle starts with conceptualization. In this phase, ideas for new medical products and treatments are brainstormed, evaluated, and selected to move on to the clinical development phase. By engaging patients early in the process, medical developers can gain valuable insights into patient perspectives, preferences, and priorities, ensuring that the product’s concept aligns with actual patient demands. Incorporating patient self-reported data during this phase can be instrumental in identifying unmet patient needs and provide a good foundation for creating a desirable final product that patients will want.

2. Feasibility Assessment: 

During the planning stage, the design is conceptualized. The concept is based on customer needs and technical requirements. The feasibility of the concept is assessed. Product developers evaluate the practicality and viability of their proposed medical product.  There is a prototype analysis, various go-to-market strategies are developed, and an initial testing takes place in order to collect user feedback. The patient’s perspective regarding the product’s design is taken into consideration before moving on to further development steps.

3. Design and Development:

Patient self-reported data plays a pivotal role in the design and development phase. PROs better capture negative, uncomfortable, and impactful symptoms from the patient’s perspective, such as nausea and anxiety. Therefore, they are better at identifying potential risks and side effects, enabling developers to proactively address safety concerns. Incorporating patient perspectives can enhance the usability, functionality, and overall patient experience of the medical product.

4. Validation/Clinical Trials:

Clinical trials are critical in determining a medical product’s safety and efficacy. By utilizing patient self-reported data in these trials, developers can assess patient outcomes and experiences with the product under real-world conditions. PROs measure the subjective elements of patients’ conditions, including health-related quality of life, pain intensity, activity limitations, participation restrictions, satisfaction or adherence to treatment and help to evaluate the burden of disease and treatment from patients’ perspectives. Integrating PROs into clinical trial data not only enhances the data’s comprehensiveness but also validates the product’s efficacy from a patient’s standpoint.

5. Launch and Regulatory Approval: 

Regulatory bodies such as the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) are increasingly recognizing the significance of patient self-reported data in evaluating product efficacy and safety. They should still be supported by objective or functional outcomes but by including PROs in clinical trial data submissions, companies can provide regulators with a more comprehensive view of the product’s benefits and potential risks.

6. Post-Market Surveillance:

Once a medical product enters the market, the company must continually monitor its safety and continue to gather patient feedback. Once on the market, it will be sold and used by a more diverse population and therefore may lead to unexpected results.  PRO is one kind of data that researchers can use to track adverse events, product shortcomings, and areas for improvement. Analysis of this feedback can be used for continuous product updates that provide better results for unmet patient needs.

By capturing patients’ experiences and perspectives, patient self-reported data can enrich a clinician’s understanding of the patients’ experience with unique information that could not be gained from biomedical outcomes alone. Integrating PROs into the product development life cycle allows for improved clinical outcomes, accelerated regulatory approvals, and faster product improvements. Companies should embrace patient-centric approaches as they will not only lead to more effective and user-friendly treatments, but also foster stronger patient-company relationships built on trust and empathy.

Through Carenity, a dynamic online community encompassing +500,000 patients and caregivers, EvidentIQ offers direct, compliant, and timely access to real-world patient data. Leveraging our Carenity platforms, we deliver a distinctive range of solutions, including Real-World Evidence, Clinical Trial Optimization, and Scientific Communication and Publications.

Carenity creates a supportive space by uniting individuals facing similar conditions, fostering connections through features like newsfeeds, discussion forums, private messaging, and more. We recognize the significance of technology in combating isolation and enhancing information sharing among members.

We have imagined an innovative model based on collaboration and honesty. On Carenity, patients are invited to express their expectations based on their conditions and on their treatment, so that they can be taken into consideration by those who will be developing the enhanced health solutions for patients.

Sources:

https://www.americanpharmaceuticalreview.com/Featured-Articles/594397-The-Lifecycle-from-Drug-Development-Through-Approval-Processes/#:~:text=The%20average%20lifecycle%20of%20drug,about%2010%20to%2012%20years.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1629006/

https://www.americanpharmaceuticalreview.com/Featured-Articles/594397-The-Lifecycle-from-Drug-Development-Through-Approval-Processes/#:~:text=The%20average%20lifecycle%20of%20drug,about%2010%20to%2012%20years.

https://learn.marsdd.com/article/product-development-lifecycle-new-drug-development/

https://toolbox.eupati.eu/resources/patient-reported-outcomes-pros-assessment/

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6219423/

https://www.bsigroup.com/en-US/medical-devices/Our-services/product-lifecycle/

https://ep.bmj.com/content/105/3/185

https://toolbox.eupati.eu/resources/patient-reported-outcomes-pros-assessment/

https://jpro.springeropen.com/articles/10.1186/s41687-020-00219-4

In the field of Health Technology Assessment (HTA), understanding the preferences of patients, healthcare professionals, and other stakeholders is crucial for making informed decisions. One valuable tool for capturing these preferences is the Discrete Choice Experiment (DCE). This article aims to explore what a DCE is, its value in HTA, the main steps involved in implementing a DCE, and best practices when building a DCE. By following these steps, researchers can effectively elicit and analyze preferences, leading to better-informed decision-making processes.

What is a DCE?

A Discrete Choice Experiment (DCE) is a quantitative research method used to assess and measure preferences. It presents participants with a set of hypothetical choices between different health interventions or treatment options, each with different attributes, and asks them to state their preferred option. By analyzing these choices, researchers can determine which attributes are most important to patients and stakeholders and how they weigh the trade-offs between different attributes.

What is the value of DCE?

Patient Preference Studies (PPS) like DCE can be implemented in each step of the medical development process life cycle in various ways. For example, DCE provides valuable insights into patient needs during the discovery phase and can also help with trial design during the clinical development phase. Preferences can also be used to evaluate the value of healthcare interventions and during the HTA phase, stakeholders can use these insights to allocate resources more efficiently and effectively to develop treatments with preferred attributes.

Main steps to implementing a DCE:

Step 1. Define the research question: Clearly articulate the research objectives and the specific preferences to be measured. Identify the target population and relevant attributes that influence decision-making.

Step 2. Design the choice sets-choose the attributes and identify levels: Develop choice sets that represent the alternatives. Define the attributes and their levels based on a thorough literature review, expert input, and stakeholder engagement. Ensure that the combinations of attribute levels are realistic and representative of the decision context. Consider the appropriate number of choices to balance the respondent burden and statistical efficiency.

Step 3. Pilot testing: Before conducting the main study, it is essential to pilot test the DCE design. This helps identify any issues with the questionnaire, refine the attribute descriptions, and ensure that the choice sets are understandable and realistic to respondents.

Step 4. Sampling and data collection: Determine the appropriate sample size and sampling strategy based on the research question and target population. Consider the mode of data collection, such as online surveys, face-to-face interviews, or telephone interviews, based on the target population and available resources. Use the appropriate data collection method to minimize biases and maximize response rates.

Step 5. Data analysis and interpretation: Employ appropriate statistical techniques to analyze the data and estimate preference models. There are three main types of models to choose from. The first is a model to estimate preference weights conditional importance of attributes. The second model identifies groups with similar treatment preferences. The last one is an estimation of willingness to pay. After running the various models, interpret the results in the context of the research question. Provide clear and concise summaries of the findings, including the relative importance of attributes.

Best practices when building a preference study using DCE

• It is important to involve stakeholders, such as patients, caregivers, and healthcare professionals, in the design process to ensure that the research question and the attributes of interest are relevant and meaningful. This can be achieved through focus groups, interviews, or surveys.
• The experimental design should be simple, the number of attributes and levels should be kept to a minimum to avoid overwhelming the participants. Generally, the number of attributes to evaluate is between 5 and 8. The main categories of treatment attributes are: Benefits, Risk, and Treatment Modalities.

• The questions should be written in a way that reduces biases.  For example, there should be neutrality in phrasing the questions and each attribute should show up an equal number of times in the DCE.
• Clear instructions and guidance should be provided to the participants to ensure that they understand the purpose of the study and how to complete the DCE. It is important to explain the concept of trade-offs and the hypothetical nature of the choices.

Discrete Choice Experiments (DCEs) are valuable tools in the field of Health Technology Assessment (HTA) for eliciting and measuring preferences. By following the main steps outlined above and adhering to best practices, researchers can effectively design and implement preference studies using DCEs. The insights gained from DCEs contribute to evidence-based decision making, helping policymakers allocate resources and make informed choices that align with the preferences of patients, healthcare professionals, and other stakeholders.

EvidentIQ can provide support when conducting DCE studies. From the experimental design stage to implementation and reporting, EvidentIQ can customize a solution that can help you effectively execute your patient preference study. They offer best-in-class Real World Evidence (RWE) methodologies, such as DCE, for patient studies in multiple diseases and geographical areas thanks to their direct access to a global patient platform. Patient studies can focus on treatment preference, quality of life, value of health, disease/treatment burden, unmet needs, etc. EvidentIQ can help generate unique Real World Data (RWD) to significantly help life sciences customers support the value story of their product for HTA submission, pricing and reimbursement as well as their scientific communication within the clinical community.

Sources: 

https://www.youtube.com/watch?v=IPIkIXWOJ5g
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8546533/#:~:text=A%20discrete%20choice%20experiment%20

https://yhec.co.uk/glossary/discrete-choice-experiment-dce/

https://bmjopen.bmj.com/content/11/3/e045803

https://systematicreviewsjournal.biomedcentral.com/articles/10.1186/s13643-021-01647-z

In collaboration with DT Consulting, Carenity*, part of EvidentIQ, conducted a survey of patients whose primary condition was ankylosing spondylitis, bipolar disorder, breast cancer, chronic obstructive pulmonary disease, depression, diabetes, multiple sclerosis, Parkinson’s disease, or rheumatoid arthritis,  to see if pharma channels are performing well enough for patients.

As patients seek to become more informed and involved in their own healthcare, their health outcomes are closely tied to the pharmaceutical industry’s ability to engage with them. To assess the current state of this relationship, we surveyed patients in Europe and the U.S. so that our partner DT Consulting could put patients in the role of customer and applied our Customer Experience Quotient® (CXQ®) metric to see which CX efforts satisfied or disappointed patients.

In particular, we asked the patient:

What did you do when you last engaged with pharma-created services or information?

How do you most prefer to interact with pharmaceutical firms, and what kind of interaction do you actually have most often?

What was the most recent pharma-created service or information you used?

After your last engagement with pharma-created services or information, what did you do?

How did your most recent interaction with a pharmaceutical company affect your view of the following?

Has COVID-19 made you more or less likely to engage with pharma in the future?

Access the complete report

*What is Carenity?

Carenity by EvidentIQ is our leading social platform supporting +500,000 patients and caregivers worldwide, across +1,200 chronic and rare diseases. Carenity provides direct, timely and compliant access to patient real-world data. Our Carenity platforms help fulfill our unique offer for Real-World Evidence, Clinical Trial Optimization, as well as Scientific Communication and Publications.

Carenity connects people suffering from the same conditions by providing them with a free social network: friends, newsfeed, discussion forums, private messaging, etc. We are convinced that new technologies help members feel less alone and better informed.

Find out more.

In collaboration with DT Consulting, Carenity*, part of EvidentIQ, conducted a survey of patients from France, Germany, Italy, Spain, the UK, and the US to learn more about their experience with clinical trials.

Understanding the difference between participants’ expectations and their actual experiences is a key issue in advancing medical research.

What are patients’ expectations in terms of communication from the organization managing the clinical trial during a clinical trial?
How is the patient informed about the trial? And by which health actor first?
What channels of communication does the patient use most with the organization managing the clinical trial?
What factors have the most impact on a patient’s decision to complete a clinical trial?

Access the complete report

*What is Carenity?

Carenity by EvidentIQ is our leading social platform supporting +500,000 patients and caregivers worldwide, across +1,200 chronic and rare diseases. Carenity provides direct, timely and compliant access to patient real-world data. Our Carenity platforms help fulfill our unique offer for Real-World Evidence, Clinical Trial Optimization, as well as Scientific Communication and Publications.

Carenity connects people suffering from the same conditions by providing them with a free social network: friends, newsfeed, discussion forums, private messaging, etc. We are convinced that new technologies help members feel less alone and better informed.

Find out more.

I’m Gilda Teissier, Marketing Director at EvidentIQ and I recently met with Thomas Verjus, Global Business Developer at Carenity, part of the EvidentIQ Group.

Decentralized clinical trials (DCT) are one of the latest clinical research innovations and are defined by Mckinsey as studies “centered around patient needs that improve the patient experience. The focus of such a trial is on making it more convenient, closer to the patients, or both by using a combination of virtual and physical elements to conduct the required trial procedures.”

The COVID-19 pandemic accelerated dramatically the adoption of DCT. In this context, many health regulators (FDA, EMA, MHRA,…) adopted urgent and temporary guidance to allow sponsors to adapt their current protocol and be able to conduct clinical trials remotely. But as time passes, all these regulators have realized that the use of “virtual elements” has grown in parallel with acceptance of “virtual medicine,” accelerating shifts in clinical trial design that many feel are long overdue. The shift has been reinforced by growing digital health technologies and also a rising interest in patient centricity from sponsors.

The following interview will give us an overview on the current DCT landscape and their importance, as well as an insight on the EvidentIQ Way.

Gilda: Why are DCT important?

Thomas: Because they help bring new treatments faster on the market and make it easier to reach demographics which would not have been thought attainable before. In a fully decentralized clinical trial, there is a minimum or even no need to go to a study center, which can accelerate the process and give more options on a pandemic, or not, context. Comparing to traditional clinical research, DCT will allow to speed up patient enrollment, increase patient retention and collect better quality of evidence thanks to remote data collection.

Gilda: How are DCT changing the clinical trial landscape?

Thomas: A few clinical trials were already using some virtual components before 2019. DCT have become a growing market since the COVID-19 pandemic. Recent first guidance published by health authorities are also encouraging a rising DCT adoption. In 2022, it is expected that there will be around 1,300 decentralized trials and/or with a virtual component. This will mean an increase of 28% compared to 2021. So, it is pretty clear that if you want to conduct a successful trial this year, DCT is your answer as it appears to be the new gold standard for the industry.

“In 2022, it is expected that there will be around 1,300 decentralized trials and/or with a virtual component.”

Gilda: Which are the main benefits of DCT?

Thomas: There is a long list of DCT benefits, but in a nutshell, I think we can say that the first and most important is the easy access to patients wherever they live. You can conduct online patient recruitment and hence have faster patient enrollment, especially adapted for some specific diseases. This also helps to have better patient engagement and less drop-out as every step of the trial is easier for them, avoiding burden of onsite visits.

Aside from that we can also mention that DCTs have higher data quality for the sponsor, real-time monitoring, less source data verification, fewer intermediaries, a predefined and compliant format of data, among other things.

Benefits of DCT are “easy access to patients wherever they live. […] better patient engagement […] higher data quality for the sponsor…”

Gilda: Which are the challenges of conducting a DCT?

Thomas: Even though they are the future of clinical trials, there are still some issues that need to be perfected. Some conditions may prevent doing DCT even in hybrid mode, not all the population may have access to the necessary technology or might not be tech oriented. At the same time some of the exams might only be conducted at a hospital. And this is without talking about specific ethical or data privacy regulations.

Before starting a DCT, it’s key to be pragmatic here and ask the good questions to define the most adapted DCT strategy – hybrid of fully decentralized depending on virtual components to be selected.

However, despite this, DCT are worth the while and they are proving to be faster and more effective as time passes while bringing a great option to the table, a more diverse panel.

Gilda: Is it true that the costs of running a DCT are lower?

Thomas: Yes, it is, DTCs are a real game-changer, the time & costs to run a trial are substantially reduced thanks to the model. You only need to remember that every aspect of the trial needs to be thought through, so it can go smoothly and save you money.

“DTCs are a real game-changer, the time & costs to run a trial are substantially reduced thanks to the model.”

Gilda: How is EvidentIQ a key player in DCT?

Thomas: At EvidentIQ we offer a direct access to patients with a global platform of 500k patients on 1,200+ conditions to design a patient centric DCT and speed up patient enrollment. We also combine best-in-class technologies and data sciences services to offer an innovative DCT experience. We have a Hybrid or 100% digital model with fully digital CRO services and innovative data sources to optimize your clinical research.

Gilda: Thanks a lot for your time.

For more information and a deeper understanding on this topic be sure to check out our upcoming webinar on this topic which will take place on March 10th at 5pm CET.

I’m Gilda Teissier, Executive Marketing Director at EvidentIQ and I recently met with Lise Radoszycki, COO at Carenity and Global Head of Data Science at EvidentIQ.

Over the past decade, we’ve observed a rise in patients’ power and engagement regarding their own care. 

The concept of Evidence-based medicine has also emerged with the goal of delivering the right care at the right time to the right patient. One of the core concepts of Evidence-based medicine is patient preference.

Industry, regulators, HTA bodies and players are showing more and more interest in the use of patient preference studies. The goal is to help incorporate the patient perspective into clinical drug development, care management, and healthcare decision-making.

In this context, most of the pharmaceutical companies want to be patient-centric, but it’s not always clear how and when to include patient perspective into the product development cycle.

The following interview with Lise Radoszycki, COO at Carenity and Global Head of Data Science at EvidentIQ, will give us an overview of preference study methodologies focusing on Discrete Choice Experiments (DCE), and will share light on how to optimize DCE design to improve the quality on patient studies.

Gilda: First of all, could you explain to us what a preference study is?

Lise: Of course, patient preference studies are based on patient preference information, that reflects what treatment attributes matter to patients, how much these matter to them and how they make trade-offs between treatments attributes.

Describing trade-offs allows us to evaluate compromises done by patients when they select their preferred treatment option. To illustrate this concept, I will ask you 3 questions:

1. Do you prefer to be rich or to be poor? I guess that your answer will be rich. In the same way, if I ask you, do you prefer to be sick or to be healthy? Again, the answer is easy, you will mostly say “healthy.”

2. Now, if I ask you “Do you prefer to be rich and sick or poor and healthy?” The choice is more complicated, and you will need to make some compromises or trade-off.

For each of these 3 types of information, qualitative or quantitative methodologies exist. Qualitative methods are mainly based on focus groups or semi-structured interviews, whereas quantitative methods are mainly based on questionnaires administered directly to patients.

Among the quantitative methods there are two main types of methodologies: the revealed preference methods and the stated preference methods.

Unlike revealed preference techniques that use observations on real choices made by respondents, stated preference methods ask patients how much they value something. This type of methodology allows to evaluate a treatment that is not yet on the market or a drug that targets a restricted population of patients such as a rare disease or a specific disease subtype. Stated preference methods are widely used in healthcare. In this type of method, the subject is asked how much they value something and here is where the Discrete Choice Experiment comes in.

Gilda: What is a Discrete Choice Experiment?

Lise: A DCE is a stated preference method increasingly used in healthcare to elicit patient preferences. In a DCE, participants are asked to state their preferred choice between several competing treatment alternatives. In general, there are two treatment options. The features of these treatments are called attributes. Each attribute is decomposed into levels. Each treatment alternative presented to participants is a combination of treatment attributes and levels. The question asked to patients might be: “If you were offered these two treatments, which one would you choose?”

“In a DCE, participants are asked to state their preferred choice between several competing treatment alternatives.”

Gilda: And which would be the main questions that need to be asked in order to develop a successful DCE?

Lise: For me there are three questions that help create the best study design. You can use these best practices for all patient studies, not just preference studies.

The first question is: Which research question is best suited for the strategic objectives to be achieved by measuring patient preferences?

The second: Which study population would be most appropriate to answer the defined research question?

Finally: What is the most appropriate methodology to answer the research question while considering the operational constraints?

Gilda: If we take this into consideration, it would mean that the DCE is not the right method for every preference study?

Lise: Even if the DCE is considered as a gold standard, it is not always the most appropriate method. Indeed, it is important to highlight that the main guidelines on preference studies published by the NICE, the FDA or ISPOR do not impose a particular methodology, but propose criteria for assessing the quality of patient studies.

For example, if the goal is to evaluate preferences regarding several topics (Qol, care pathway) the DCE is not the most appropriate methodology. Same if you want to evaluate the perception of a specific treatment option testing some pre-defined hypothesis. It is also very important to consider the operational constraints such as the prevalence of the study population, the timelines, the budget allocated to the study, etc.

Gilda: There are 5 key steps to a preference study with DCE methodology, could you tell us more about them?

Lise: Briefly, we could say that the first step is to identify the attributes of the treatment. The second, is to identify and define the levels of each attribute with methods such as literature research, and qualitative interviews. The third step is to create scenarios and choice sets with an experimental design method, which will be used to select a reduced sample of choices (named “fractional factorial” design). The fourth step is Statistical Modeling based on the random utility model. And the final key to a DCE study is the interpretation of results, where it is crucial to identify those that have a negative or positive impact on the preference of a treatment. Patient preference studies generate insightful data that can be used at each step of the medical product life cycle. 

Gilda: Do patient preference studies really make a difference in regulatory decision-making?

Lise: This is a very interesting question. Several Authorities (such as NICE or FDA) invite companies to start a conversation with them about using patient preference information to support their submission. Over the past years, several guidelines and initiatives have been developed to implement and improve quality standards (such as PREFER or IMI).

There is therefore, a real desire to include patient studies in regulatory decision-making. Nevertheless, the level of consideration given to this type of study is very variable and highly depends on the quality of the study.

For example, Janssen included in its dossier a preference study to support Esketamine, a treatment for resistant depression. The goal of this DCE study was to assess patient perspective on benefit-risk trade-offs. In their HTA dossier, outcomes importance was reinforced by the results of the patient study. The advisory committee of the FDA said that it was taken into account in their decision, but we do not know how much influence it really had at the end.

Gilda: Before leaving, could you give us some final tips to a successful DCE study?

Lise: First, it is really important to make sure your participants fully understand the study materials to reduce uncertainty caused by health numeracy/literacy.

For this purpose, you can use a pilot study or a cognitive debriefing to pre-test the questionnaire and the material associated with the study with a limited number of patients.  

The second advice would be to ensure representativeness of the sample and generalizability of results.

Another important thing to remember is that in order to improve the quality of the results, it is crucial to minimize the potential cognitive biases, such as framing bias, anchoring bias, ordering or labelling effects or simplified heuristics.

The final advice that I can give is to think carefully about the statistical analysis plan and data management plan in order to ensure logical soundness and robustness of data analysis.

Gilda: Thank you for your time.

A webinar addressing this same topic was recently conducted by Carenity and Takeda through the Xtalks platform. If you want to watch the replay you can access it by clicking here: ACCESS WEBINAR

For more information about EvidentIQ’s and Carenity’s solutions click here.

I’m Gilda Teissier, Executive Marketing Director at EvidentIQ and I recently met with Michael Chekroun, Founder and CEO at Carenity and Chief Strategy and Transformation Officer at EvidentIQ Group for this interview.

COVID-19 has been among us for more than a year now and it has impacted the life of every person in the world. It goes without saying that certain populations have been more affected than others.

Carenity, part of EvidentIQ group, is a leading digital patient platform with 500,000 patients and caregivers worldwide. In the context of COVID-19, Carenity’s mission has been more than ever to support its patient community and to help healthcare actors quickly access the best possible data to provide the highest quality of care to patients. In this sense, Carenity has set up an online survey to measure the impact of COVID-19 on access to care and quality of life in real-time for chronic patients, a population that is particularly vulnerable in a context of pandemic and lockdown. While attention is primarily focused on those affected by COVID-19, the risk of poor, insufficient access to care for chronic patients is real, with potential dramatic consequences. Will chronic patients be the collateral victims of the epidemic?

This survey was conducted at three different periods in the following 6 countries: France, the UK, Germany, Italy, Spain, and the US. The first survey was conducted during the first lockdown in Q2 2020, the second after the summer 2020, and the last from December 2020 until April 2021. The results from all three surveys have been shared with both the patient and healthcare communities. Today we have the opportunity to get some deeper insights on the key results thanks to Michael Chekroun, Founder and CEO of Carenity and Chief Strategy and Transformation Officer at EvidentIQ Group.

Gilda: Michael, what made you conduct this survey in the first place? and who did you interview? Was it a special group?

Michael: The pandemic suddenly sent the world into a state of shock in early 2020. The sense of uncertainty was amplified by the lockdown measures that followed in many countries. At Carenity, we wanted to know what impact this crisis would have on patients with chronic illnesses, both in terms of access to care and impact on their quality of life, to find quick solutions to protect an already fragile population.

“We wanted to know what impact this crisis would have on patients with chronic illnesses“

Gilda: One of the central questions was about patients’ intention to be vaccinated. What did you find out about people’s willingness to get vaccinated and what do you think are the reasons?

Michael: We surveyed over 3,500 people in Europe and the United States. The results show that there is a strong motivation to get vaccinated. 48% of respondents said it is because they were at risk, 46% wanted to protect others and 41% just wanted to protect themselves from the virus. That said, the survey indicates that the willingness to be vaccinated differs massively between countries.

“The willingness to be vaccinated differs massively between countries”

Gilda: When it comes to medical consultations and healthcare professionals, have you seen an improvement in the situation with regard to medical visits or procedures being canceled or postponed since the first lockdown? What do you think is the reason for this?

Michael: Nearly half of those surveyed reported that their medical visits or procedures were canceled or postponed during the 1st wave of the virus, during the spring of 2020, while 75% reported experiencing the same difficulties in late 2020 and early 2021. It is difficult to estimate the long-term impact, but many physicians are already concerned about the deteriorating health of their patients.

Gilda: There are many digital solutions now offering teleconsultation services. Based on the barometer’s findings, do you think there is a real market out there for video consultation platforms?

Michael: Yes, absolutely. Roughly 67% of patients in the US would be willing to use telehealth consultations and in Europe 47% of respondents are open to telehealth. I see a bright future for these innovative services, as the COVID-19 crisis has accelerated the adoption of telehealth services by the general public and health care professionals.

Gilda: Physical and mental health have been an important element and a casualty of this pandemic. Have these two aspects also worsened for people living with chronic illnesses and to what extent?

Michael: The pandemic is expected to have a long-term effect on the mental health of people populations around the world, especially the youngest and most fragile. Again, it is difficult to estimate the long-term impact, but we can already see that the number of digital solutions related to mental health is booming. It is one of the most followed subjects by VCs in the US.

Gilda: Information is essential when faced with uncharted territory such a pandemic, and most of all when living with a chronic condition. Has people with chronic conditions been given enough information? What do patients think?

Michael: Information is not the problem. We have been literally inundated with information about the virus, its impact, its variants and its treatments. So much so that everyone feels able to judge the effectiveness or the safety of a particular vaccine. Moreover, a majority of the patients surveyed consider themselves well informed about the virus or the vaccines. The challenge now is to understand the long-term impact of this virus, the effects of which can last several months and which experts now call “long COVID”. Even if we can see the light at the end of the tunnel thanks to vaccination, we must remain cautious in a field where knowledge evolves daily.

“The challenge now is to understand the long-term impact of this virus”

Gilda: Thank you Michael

Do you want to discover all the results? Join us on June 17 at 17h CET for our webinar on The Impact of COVID-19 on Patients with Chronic Illnesses! Register here